Spectral Clustering Algorithms

Version (4.78 KB) by Asad Ali
Implementation of four key algorithms of Spectral Graph Clustering using eigen vectors : Tutorial


Updated 6 May 2017

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The code for the spectral graph clustering concepts presented in the following papers is implemented for tutorial purpose:
1. Ng, A., Jordan, M., and Weiss, Y. (2002). On spectral clustering: analysis and an algorithm. In T. Dietterich, S. Becker, and Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems 14 (pp. 849 – 856). MIT Press.

2. P. Perona and W. T. Freeman, "A factorization approach to grouping",In H. Burkardt and B. Neumann, editors, Proc ECCV, pages 655-670, 1998.

3. J. Shi and J. Malik, "Normalized Cuts and Image Segmentation", In Proc. IEEE Conf. Computer Vision and Pattern Recognition, pages 731-737, 1997.

4. G.L. Scott and H. C. Longuet-Higgins, "Feature Grouping by Relocalisation of Eigenvectors of the Proxmity Matrix", In Proc. British Machine Vision Conference, pages 103-108, 1990.

Evolution of spectral clustering methods and the various concepts proposed by the above authors are demonstrated in this implementation.

Cite As

Asad Ali (2023). Spectral Clustering Algorithms (https://www.mathworks.com/matlabcentral/fileexchange/26354-spectral-clustering-algorithms), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R14
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Version Published Release Notes

no change (version 1.0 was first released on 12-Jan-2010)
A comment in the file Shi_Malik has been updated to avoid confusion.
Another file Jordan_Weiss has been renamed to Ng_Jordan_Weiss
The output of all files remains the same.